Method and apparatus for determining the operational state of a navigation system

ABSTRACT

A method of determining an operational state of a navigation system of a platform is provided. The method comprises testing at least a portion of hardware in the navigation system. Additionally, a measurement of at least one navigation variable from an inertial sensor is combined with a measurement of another navigation variable. A plurality of residuals for the measurement of at least one navigation variable and the measurement of another navigation variable are determined with a blending filter. An error for the measurement of at least one navigation variable is estimated based on the plurality of residuals. The method also predicts an error for the measurement of at least one navigation variable while the navigation system is in route. A determination is made as to whether the navigation system meets operational standards based on testing at least a portion of hardware, estimating an error for the measurement of at least one navigation variable, and predicting an error for the measurement of at least one navigation variable. Finally, one of a first state and a second state of the navigation system is output, the first state indicating that the navigation system does meet operational standards and the second state indicating that the navigation system does not meet operational standards.

BACKGROUND

A platform's position, velocity, and/or attitude (as well as othernavigation variables) are often determined by a navigation system. Thenavigation variables can then be used by either a human operator or acontrolling algorithm to direct the movement of the platform. Thenavigation variables are often determined by inertial sensors. Inaddition to the inertial sensors, aiding source(s), such as a globalposition system (GPS) receiver, may be used to supplement the datareceived from the inertial sensors. Inertial sensors providemeasurements, such as specific force and angular velocity, asexperienced by the host platform. A GPS receiver can independentlyprovide additional platform navigation variables using signals receivedfrom GPS satellites. The navigation variables received from the inertialsensors and the aiding device(s), however, may contain errors becauseof, for example, open loop measurements by the inertial sensors or noisein the signals from the GPS satellites.

SUMMARY

The following summary is made by way of example and not by way oflimitation. In one embodiment, a method of determining an operationalstate of a navigation system of a platform is provided. The methodcomprises testing at least a portion of hardware in the navigationsystem. Additionally, a measurement of at least one navigation variablefrom an inertial sensor is combined with a measurement of anothernavigation variable. A plurality of residuals for the measurement of atleast one navigation variable and the measurement of another navigationvariable are determined with a blending filter. An error for themeasurement of at least one navigation variable is estimated based onthe plurality of residuals. The method also predicts an error for themeasurement of at least one navigation variable while the navigationsystem is in route. A determination is made as to whether the navigationsystem meets operational standards based on testing at least a portionof hardware, estimating an error for the measurement of at least onenavigation variable, and predicting an error for the measurement of atleast one navigation variable. Finally, one of a first state and asecond state of the navigation system is output, the first stateindicating that the navigation system does meet operational standardsand the second state indicating that the navigation system does not meetoperational standards.

DRAWINGS

FIG. 1 is a block diagram of one embodiment of a navigation system fordetermining a navigation state of a platform;

FIG. 2 is a block diagram illustrating one embodiment for determining anavigation state of a platform; and

FIG. 3 is a flow chart illustrating one embodiment of a method fordetermining a navigation state for a platform.

In accordance with common practice, the various described features arenot drawn to scale but are drawn to emphasize specific features relevantto the present disclosure.

DETAILED DESCRIPTION

FIG. 1 is one embodiment of an apparatus 100 for determining anoperational state of a navigation system. In the embodiment illustratedin FIG. 1, apparatus 100 comprises a navigation system (referred toherein as “navigation system 100”). FIG. 1 includes a processing system102 which can be implemented in various form factors and configurationsincluding, for example, as a portable computer or can be embedded in (orotherwise incorporated in or communicatively coupled to) otherelectrical systems or devices.

Processing system 102 includes at least one programmable processor 104(referred to herein as “processor 104”). In one embodiment, processor104 comprises a microprocessor. Processor 104 executes various items ofsoftware. The software comprises program instructions that are embodiedon one or more items of processor-readable media. For example, in oneembodiment, processor readable media includes a hard disk drive or othermass storage device local to the processing system 102 and/or sharedmedia such as a file server that is accessed over a network such as alocal area network or wide area network such as the Internet. In oneembodiment, the software is firmware which is embedded in a storagemedium within processor 104. In the embodiment shown in FIG. 1, thesoftware executed by processor 104 is embodied on a storage medium 106which is communicatively coupled to processor 104.

In one embodiment, navigation system 100 is included within a platform.For example, in one embodiment, the platform in which navigation system100 is included is a guided munition that is launched from an aircraft.In other embodiments, navigation system 100 is included within guidedmunitions launched from land, sea, or other air based vehicles ordirectly from a land based station. Additionally, in yet otherembodiments, navigation system 100 is included within other land, water,or air based platforms, including but not limited to wheeled vehicles,boats, aircraft, and spacecraft. In one embodiment, the platform isfully autonomous. In an alternative embodiment, the platform ispartially autonomous and partially human controlled. In still otherembodiments, the platform is fully human controlled.

Navigation system 100 determines navigation related measurements (alsoreferred to herein as “navigation variables”) for the platform that canbe used to control the movement of the platform. Navigation system 100determines navigation variables through the use of a plurality ofmeasurement devices 107. In the embodiment shown in FIG. 1, theplurality of measurement devices includes an inertial sensor suite 109and an aiding source 1 12. Aiding source is a device that can performnavigation related measurements independent of inertial sensor suite109. In this embodiment, the inertial sensor suite 109 comprises threeaccelerometer-gyroscope pairs each pair comprising an accelerometer 108and a gyroscope 1 10. In other embodiments, other motion sensing devicesare used to determine the movement of the platform and/or a differentnumber of accelerometers and/or gyroscopes are used. Additionally, inthis embodiment, aiding source 112 comprises a GPS receiver, although inother embodiments, other aiding devices are used.

Processing system 102, accelerometers 108, and gyroscopes 110 togethercomprise an inertial navigation system for the platform. Accelerometers108 measures the specific force experienced by the platform, andcommunicates the measured data (navigation variables) to processingsystem 102. Gyroscopes 110 measures the angular velocity experienced bythe platform and also communicates the measured data (navigationvariables) to processing system 102. Processing system 102 receives thenavigation variables of specific force and angular velocity fromaccelerometers 108 and gyroscopes 110, and determines the position,velocity, attitude, and/or other variables of the platform. As known tothose skilled in the art, processing system 102 is initially providedwith the position, velocity, and attitude of the platform and then usesthe navigation variables received from accelerometers 108 and gyroscopes110 to determine an updated position, velocity, attitude, and/or othervariables for the platform.

As stated above, navigation system 100 also includes an aiding source112. As known to those skilled in the art, aiding source 112 determinesthe position, velocity, and/or other navigation variables of theplatform. Aiding source 112 communicates the position, velocity, and/orother navigation variables to processing system 102.

In this embodiment, the platform navigation variables are based on themeasurements from inertial sensor suite 109 and aiding source 112 isused as an aiding source to estimate errors present in the inertialsensor suite 109. In other embodiments, measurements from both inertialsensor suite 109 and aiding source 112 are blended to directly estimatethe platform navigation variables.

Due to open loop measurements and/or inaccuracies of inertial sensorsuite 109 each of the respective measurements from inertial sensor suite109 may contain a certain amount of error. As a result the calculationof position, velocity, and/or other navigation variables also maycontain a certain amount of error. These measurement errors affect theaccuracy of the platform navigation variables. Depending on the desiredpurpose and design of the platform that navigation system 100 is within,there may be a limit at which the accuracy becomes too poor toeffectively complete the task, or to complete the task with the desiredconfidence level. Additionally, the errors of inertial sensor suite 109may change over time due to changing environmental conditions,malfunctioning equipment, or other factors.

Thus, there may be situations when it is desirable to know the currentstatus (error level, accuracy, etc.) of navigation system 100, so thatdecisions and appropriate actions may be made regarding navigationsystem 100 and the platform. For example, when navigation system 100 isin a good operational state, an operator may determine that it isappropriate to send/launch the platform on its intended route. Incontrast, when navigation system 100 is in a poor operational state, anoperator may wait until the operational state improves prior tosending/launching the platform or may abort the mission altogether.Additionally, if the platform is sent during a good operational state,but navigation system 100 changes to a poor operational state duringoperation, then the platform can be halted during operation or otheraction can be taken as appropriate.

In one embodiment, processing system 102 determines the status ofnavigation system 100, and outputs an indication of the status throughan output device 114 which is coupled to processing system 102. In oneembodiment, output device 114 is coupled to processor 104. In anotherembodiment, processing system 102 outputs an indication of the status toa software program, which then takes appropriate action to outputthrough output device 114.

In one embodiment, navigation system 100 is determined to be in one oftwo states, operational or non-operational. When processing system 102determines which state (operational or non-operational) navigationsystem 100 is in, the state is output on output device 1 14. Forexample, in one embodiment, output device 114 is a light that shows agreen color for an operational state and a red color for anon-operational state. Alternatively, when output device 114 is a light,the light is unlit prior to making a determination of the state ofnavigation system 1 14. If navigation system 100 is determined to be ina non-operational state, the light remains unlit; whereas, if navigationsystem 100 is determined to be in an operational state, the light islit. In another embodiment, output device 114 is a computer monitorwhich displays the status as text or images on the monitor. In yetanother embodiment, output device 114 is a speaker, which emitsdifferent audible signals for each state of navigation system 100. Inother embodiments, the status is determined for more than two states andan indication of the determined state is provided on output device 114.

The operation of system 100 will now be described with reference toFIGS. 2 and 3. FIG. 2 illustrates one embodiment of a block diagram 200for determining and communicating the operational state of a platform.FIG. 3 illustrates one embodiment of a method 300 for determining andcommunicating the operational state of a platform. Block diagram 200 andmethod 300 can be used by navigation system 100 to determine which stateit is in by performing one or more tests and basing the determination ofoperational/non-operational on the results of the tests. For example, inone embodiment, navigation system 100 performs five tests and the systemis determined as operational if navigation system 100 passes all five ofthe tests. In another embodiment, navigation system 100 is determined asoperational if four of the five tests are passed. In other embodiments,other numbers of tests are performed and other thresholds are used fordetermining whether navigation system 100 is operational ornon-operational. Additional information regarding the tests is providedbelow with reference to FIGS. 2 and 3. Although in the embodiment ofFIGS. 2 and 3, a certain number of tests are shown and the tests areshown in a certain order, in other embodiments, more or fewer tests thanthat shown in FIGS. 2 and 3 are used, and/or the tests are conducted inan order different from that shown in FIGS. 2 and 3.

In one embodiment one test in which navigation system 100 performs, is atest of inertial sensor suite 109, such as a test of accelerometer(s)108 and/or gyroscope(s) 110 to determine whether inertial sensor suite109 is performing within a threshold. The threshold can be determinedbased on required or desired precision of movement, purpose of themission of the platform, and/or other factors.

Inertial sensor suite 109 performs a measurement on the platform atblock 202. Aiding source 112 also performs a measurement at blocks 204and 302. In one embodiment at block 304, the validity of the measurementfrom aiding source 112 is determined. For example, in one embodiment,the validity is checked based on multiple redundant measurements takenby aiding source 112. When a GPS receiver has more than 4 pseudorangemeasurements, the additional (redundant) measurements are compared toeach other to determine an error for the measurements by the GPSreceiver. Additionally, in one embodiment, multiple aiding sources (suchas another GPS receiver) are used and the measurements from each aidingsource are compared against each other to determine an error for one ormore aiding source. The redundant measurements by aiding source 112 areused to detect and isolate errors in the measurements provided by aidingsource 1 12. Methods to detect and isolate errors are known to thoseskilled in the art (often referred to as Receiver Autonomous IntegrityMonitoring [RAIM] in GPS receivers or parity space methods in general)and are not described in more detail herein.

At block 306, the measurements from inertial sensor suite 109 are thenconverted to navigation variables equivalent to the navigation variablesfrom aiding source 112. This enables the measurements from inertialsensor suite 109 to be compared to the measurements from aiding source112. At block 308, the navigation variables from the inertial sensorsuite 109 are then differenced with the navigation variables from aidingsource 1 12. The resultant difference is presented to a blending filter206 located in processing system 102. For example, in this embodimentthe blending filter is a Kalman filter.

The Kalman filter has knowledge of previous errors which were present ininertial sensor suite 109. The Kalman filter also knows what errorsources are present and maintains the information about errors in itsstate vector. The Kalman filter knows how these errors behave over timeand in response to stimulus and this information is contained in theprocess model. Furthermore, the Kalman filter knows the relationshipbetween the state vector and the navigation variables received. Based onthis, at block 310, the Kalman filter forms measurement residuals. Themeasurement residuals indicate how much error is present in the statevector. The Kalman filter takes the information contained in themeasurement residuals and distributes it across the state vector usingthe Kalman gain. This is used to estimate the error in the measurementsfrom inertial sensor suite 109. The Kalman filter models the amount oferror in the difference measurement received and in the state vector. Atblock 312, this information is used to form the Kalman gain and it isalso used to assess the validity of the measurement residuals. TheKalman filter compares the amount of error in the measurement residualto the amount of error that the Kalman filter modeled. If the two errorsare consistent with each other (within a certain threshold), then theresiduals are determined to be valid and the test is passed. If the twoerrors are not consistent with each other (exceeding a certainthreshold), then the residuals are determined to be invalid and the testis failed.

At block 314, the valid measurement residuals are then used to estimatethe inertial sensor error. At block 316, time-varying statisticalproperties of the measurement residuals are computed. At block 318, inone embodiment, navigation system 100 tests the measurement residuals todetermine if their noise is exhibiting normal noise characteristics. Themeasurement residuals pass the test if they exhibit appropriatestatistical characteristics. For example, the Root Mean Square (RMS)values of the residuals can be analyzed. Additionally, if themeasurement residuals are not “white” or if they do not have a zeromean, the filter likely has a fault and is determined to have failed thetest. On the other hand, if the measurement residuals are “white” andthey do have a zero mean, the filter is likely working properly and thetest is passed.

In one embodiment at block 320, the errors of inertial sensor suite 109are compared to the expected error range for the particular inertialsensor suite 109. Here, the errors of inertial sensor suite 109 asdetermined from the measurement residuals are checked against knownerror ranges to determine whether the particular inertial sensor suite109 is operating normally. For example, in one embodiment, the errors ofaccelerometers 108 are checked against the known range of errors for theparticular family of accelerometers to which accelerometers 108 belong.If accelerometers 108 are not acting normally, then there may besomething wrong with accelerometers 108 and it does not pass the test.For example, if the measurement received from accelerometers 108 areaccurate to within 10 milli-gs, but the particular family thataccelerometers 108 are in, typically has errors around 1 milli-g,accelerometers 108 are determined not to have passed the test. In otherembodiments, other criteria are used to determine whether the particularpart of inertial sensor suite 109 is performing normally. For example,in other embodiments, the operating environment or past operatinghistory of the particular inertial sensor suite 109 is compared againstthe current error received to determine if the particular inertialsensor suite 109 is operating normally.

At block 322, in one embodiment, navigation system 100 predicts whetherthe errors will be within a threshold while the platform is operating onits route. In one embodiment, the route of the platform is inputted orselected from a list. Based on the route, navigation system 100 predictsa forward propagation of its errors as the platform travels along theroute. Navigation system 100 then compares these errors to a thresholdto determine whether the mission would be successful. Information fromthe Kalman filter is used to estimate the errors.

In one embodiment, in order to pass this test, navigation system 100errors must be within the threshold during the entire route until theplatform reaches the destination. In another embodiment, in order topass the test, the estimated errors must operate within the thresholdfor a certain percentage of the route. In other embodiments, othercriteria, such as a varying threshold based on the length of the route,is used to determine the relationship between the estimated errors andthe threshold required to pass the test.

In some situations measurements from aiding source 112 may not beavailable due to atmospheric conditions, obstructions in the signal pathbetween the satellites and the receiver, or other conditions. Thus, inone embodiment, the forward propagation of the errors is determinedassuming aiding source 112 is operational and thus, the navigationvariables from aiding source 112 are included in Kalman filtercalculations. In another embodiment, the forward propagation of theerrors is determined assuming aiding source 112 is non-operational andthus, the navigation variables from aiding source 112 are not includedin the Kalman filter calculations. In yet another embodiment, theforward propagation of errors is determined twice. Once assuming aidingsource 112 is operational and again assuming aiding source 112 isnon-operational. In this embodiment, in order to pass the test, theerrors must be within the threshold for both aiding source 112operational and non-operational situations.

At blocks 208 and 324, in one embodiment, one test in which navigationsystem 100 performs for determining the state navigation system 100 is ahardware test (also referred to herein as the Built In Test [BIT]). Thehardware test is a basic test that checks some or all of the hardware innavigation system 100 to verify that each hardware component performsits basic operation successfully and that the various hardwarecomponents are communicating correctly. For example, in one embodiment amemory test is run in order to detect issues with the system. In thisembodiment, the hardware is determined to have passed the test whenthere are no errors with any of the hardware tested. In one embodiment,navigation system 100 is determined to be in a non-operational state ifthe hardware test is failed, regardless of the results of other tests.Hardware tests are well known in the art and are not described infurther detail herein.

At blocks 210 and 326, once the one or more tests have been conducted,navigation system 100 determines its operational state based on the oneor more tests. In one embodiment, navigation system 100 outputs anindication of its operational status via output device 114. In anotherembodiment, after processing system 102 determines the status ofnavigation system 100, processing system 102 outputs an indication ofthe status to an operator through output device 114.

In one embodiment, some or all of the previously described tests areused during production of navigation system 100 to determine whethernavigation system 100 meets production standards before leaving thefactory for shipment to a customer. In one embodiment, some or all ofthe previous tests are conducted by navigation system 100 and anoperational or non-operational determination is made and communicated tothe production tester. If navigation system 100 returns an operationalstate, then navigation system 100 continues to finish production. Ifnavigation system 100 returns a non-operational state, then navigationsystem 100 is fixed, discarded, or other appropriate action is taken.Advantageously, using the above tests to determine whether navigationsystem 100 is working properly may reduce production cost. For example,the above tests may be built-in to navigation system 100, thuspotentially eliminating some test equipment. Additionally, when thedetermination is made between one of two states (operation, ornon-operational) a quick and easy line is drawn as to which units meetproduction standards and which units do not meet production standards.

Instructions for carrying out the various process tasks, calculations,and generation of signals and other data used in the operation of themethods described above can be implemented in a program productincluding software, firmware, or other processor readable instructions.These instructions are typically stored on any appropriate processorreadable medium used for storage of processor readable instructions ordata structures. Such processor readable media can be any availablemedia that can be accessed by a general purpose or special purposecomputer or processor, or any programmable logic device.

Suitable processor readable media may comprise, for example,non-volatile memory devices including semiconductor memory devices suchas EPROM, EEPROM, or flash memory devices; magnetic disks such asinternal hard disks or removable disks; magneto-optical disks; CDs,DVDs, or other optical storage disks; nonvolatile ROM, RAM, and otherlike media; or any other media that can be used to carry or storedesired program code in the form of processor executable instructions ordata structures. Any of the foregoing may be supplemented by, orincorporated in, specially-designed Application Specific IntegratedCircuits (ASICs) or Field Programmable Gate Arrays (FPGAs). Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired and wireless) to a processor, the processor properly viewsthe connection as a processor readable medium. Thus, any such connectionis properly termed a processor readable medium. Combinations of theabove are also included within the scope of processor readable media.

The method of the invention can be implemented in processor readableinstructions, such as program modules or applications, which areexecuted by a data processor. Generally, program modules or applicationsinclude routines, programs, objects, data components, data structures,algorithms, and the like, which perform particular tasks or implementparticular abstract data types. These represent examples of program codefor executing steps of the methods disclosed herein. The particularsequence of such executable instructions or associated data structuresrepresent examples of corresponding acts for implementing the functionsdescribed in such steps.

Although specific embodiments have been illustrated and describedherein, it will be appreciated by those of ordinary skill in the artthat any arrangement, which is calculated to achieve the same purpose,may be substituted for the specific embodiments shown. It is manifestlyintended that any inventions be limited only by the claims and theequivalents thereof.

1. A method of determining an operational state of a navigation systemcomprising: testing at least a portion of hardware in the navigationsystem; combining a measurement of at least one navigation variable froman inertial sensor with a measurement of another navigation variable;determining a plurality of residuals for the measurement of at least onenavigation variable and the measurement of another navigation variablewith a blending filter; estimating an error for the measurement of atleast one navigation variable based on the plurality of residuals;predicting an error for the measurement of at least one navigationvariable while the navigation system is en route; determining whetherthe navigation system meets operational standards based on testing atleast a portion of hardware, estimating an error for the measurement ofat least one navigation variable, and predicting an error for themeasurement of at least one navigation variable; and outputting one of afirst state and a second state of the navigation system, the first stateindicating that the navigation system does meet operational standardsand the second state indicating that the navigation system does not meetoperational standards.
 2. The method of claim 1, wherein determiningwhether the navigation system meets operational standards is based ondetermining a Root Mean Square (RMS) value for the plurality ofresiduals.
 3. The method of claim 1, wherein determining whether thenavigation system meets operational standards is based on determiningwhether noise within the blending filter is exhibiting normalcharacteristics.
 4. The method of claim 1, further comprising: measuringthe at least one navigation variable with an inertial sensor; measuringthe another navigation variable with an aiding source;
 5. The method ofclaim 1, wherein predicting the error for the measurement of at leastone navigation variable while the navigation system is en route,predicts the error for the measurement of at least one navigationvariable using only measurements from inertial sensors.
 6. The method ofclaim 1, wherein predicting the error for the measurement of at leastone navigation variable while the navigation system is en route,predicts the error for the measurement of at least one navigationvariable once using only measurements from the inertial sensors andagain using measurements from inertial sensors and an aiding source. 7.The method of claim 1, wherein determining whether the navigation systemmeets operational standards is based on determining whether the errorfor the measurement of at least one navigation variable is within anexpected range for a performance class of the inertial sensor.
 8. Themethod of claim 7, wherein the system is determined to be in the firststate when no errors are found when testing at least a portion ofhardware, when the error for the measurement of at least one navigationvariable is consistent with a modeled amount, and when the error for themeasurement of at least one navigation variable is within an expectedrange of the measurement device.
 9. An apparatus for determining theoperational state of a navigation system comprising: a processor forexecuting software; an output device communicatively coupled to theprocessor; at least one inertial sensor communicatively coupled to theprocessor; a Global Positioning System (GPS) receiver coupled to theprocessor; a storage medium communicatively coupled to the processorfrom which the processor reads at least a portion of the software forexecution thereby, wherein the software is configured to cause theprocessor to: test at least a portion of hardware that takesnavigational measurements; receive a measurement of at least onenavigation variable from the at least one inertial sensor; receive ameasurement from the an aiding source; combine the measurement of atleast one navigation variable with the measurement from the aidingsource; determine a plurality of residuals for the measurement of atleast one navigation variable with a blending filter; estimate an errorfor the measurement of at least one navigation variable based on theplurality of residuals; predicting an error for the measurement of atleast one navigation variable while the navigation system is en route;determine whether the navigation system meets operational standardsbased on the test of at least a portion of hardware, the estimate of anerror for the measurement of at least one navigation variable, and theprediction of an error for the measurement of at least one navigationvariable; and output one of first state and a second state of thenavigation system on the output device, the first state indicating thatthe system does meet operational standards and the second stateindicating that the system does not meet operational standards.
 10. Theapparatus of claim 9, wherein the software is further configured tocause the processor to: determine a Root Mean Square (RMS) value for theplurality of residuals; and determine whether the navigation systemmeets operational standards based on the RMS value for the plurality ofresiduals.
 11. The apparatus of claim 9, wherein the software is furtherconfigured to cause the processor to: determine whether noise within theblending filter is exhibiting normal characteristics; and determinewhether the navigation system meets operational standards based onwhether the noise within the blending filter is exhibiting normalcharacteristics.
 12. The apparatus of claim 9, wherein the software isfurther configured to cause the processor to: predict an error for themeasurement of at least one navigation variable using only themeasurement from the at least one inertial sensor.
 13. The apparatus ofclaim 9, wherein the software is further configured to cause theprocessor to: predict an error for the measurement of at least onenavigation variable once using only the measurement from the at leastone inertial sensor, and again using the measurement from the at leastone inertial sensor and the measurement from the aiding source.
 14. Theapparatus of claim 9, wherein the software is further configured tocause the processor to: determine whether the error for a measurement ofat least one navigation variable is within an expected range for thatmeasurement device; and determine whether the navigation system meetsoperational standards based on whether the estimated inertial errors arewithin an expected range for a given inertial sensor performance class.15. A program product comprising a processor-readable medium on whichprogram instructions are embodied, wherein the program instructions areoperable to: test at least a portion of hardware that takes navigationalmeasurements; combine a measurement of at least one navigation variablefrom an inertial sensor with a measurement of another navigationvariable; determine a plurality of residuals for the measurement of atleast one navigation variable with a blending filter; estimate an errorfor the measurement of at least one navigation variable based on theplurality of residuals; predict an error for the measurement of at leastone navigation variable while the navigation system is en route;determine whether the navigation system meets operational standardsbased on the test of at least a portion of hardware, the estimate of anerror for the navigation variable, and the prediction of an error of atleast one navigation variable; and output one of first state and asecond state of the navigation system, the first state indicating thatthe navigation system does meet operational standards and the secondstate indicating that the navigation system does not meet operationalstandards.
 16. The program product of claim 15, wherein the programinstructions are further operable to: determine a Root Mean Square (RMS)value for the plurality of residuals; and determine whether thenavigation system meets operational standards based on the RMS value forthe plurality of residuals.
 17. The program product of claim 15, whereinthe program instructions are operable to: determine whether noise withinthe blending filter is exhibiting normal characteristics; and determinewhether the navigation system meets operational standards based onwhether the noise within the blending filter is exhibiting normalcharacteristics.
 18. The program product of claim 15, wherein theprogram instructions are operable to: receive a measurement of at leastone navigation variable from at least one inertial sensor; receive ameasurement from a global positioning system (GPS) receiver;
 19. Theprogram product of claim 18, wherein the program instructions areoperable to: predict an error of at least one navigation variable onceusing only the measurement from the at least one inertial sensor, andagain using the measurement from the at least one inertial sensor andthe measurement from the GPS receiver.
 20. The program product of claim15, wherein the program instructions are operable to: determine whetherthe error for a measurement of at least one navigation variable iswithin an expected range for that measurement device; and determinewhether the navigation system meets operational standards based onwhether the error for a measurement of at least one navigation variableis within an expected range for an inertial sensor.